TY - JOUR
T1 - Characterizing Alzheimer's disease using a hypometabolic convergence index
AU - Chen, Kewei
AU - Ayutyanont, Napatkamon
AU - Langbaum, Jessica B.S.
AU - Fleisher, Adam S.
AU - Reschke, Cole
AU - Lee, Wendy
AU - Liu, Xiaofen
AU - Bandy, Dan
AU - Alexander, Gene E.
AU - Thompson, Paul M.
AU - Shaw, Leslie
AU - Trojanowski, John Q.
AU - Jack, Clifford R.
AU - Landau, Susan M.
AU - Foster, Norman L.
AU - Harvey, Danielle J.
AU - Weiner, Michael W.
AU - Koeppe, Robert A.
AU - Jagust, William J.
AU - Reiman, Eric M.
N1 - Funding Information:
This work was also partly supported by grants from the National Institute on Aging ( R01AG031581 , P30AG19610 , R01AG025526 ), the National Institute of Mental Health ( R01MH057899 ), the Evelyn G. McKnight Brain Institute (G.E.A.), the state of Arizona (E.M.R., R.J.C., G.E.A., K.C.), and contributions from the Banner Alzheimer's Foundation and Mayo Clinic Foundation .
PY - 2011/5/1
Y1 - 2011/5/1
N2 - This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18. months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p=9e-17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7.38 and 6.34, respectively), and those with both had an even higher HR (36.72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.
AB - This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18. months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p=9e-17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7.38 and 6.34, respectively), and those with both had an even higher HR (36.72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.
KW - Alzheimer's disease
KW - FDG
KW - Hippocampal volume
KW - Hypometabolic convergence index
KW - MCI
KW - PET
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U2 - 10.1016/j.neuroimage.2011.01.049
DO - 10.1016/j.neuroimage.2011.01.049
M3 - Article
C2 - 21276856
AN - SCOPUS:79953041925
SN - 1053-8119
VL - 56
SP - 52
EP - 60
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -